<![CDATA[Predictive analytics has emerged as a critical component of modern healthcare management due to its ability to transform healthcare data into actionable insights that support operational and clinical decision-making. This systematic literature review synthesizes current evidence regarding predictive analytics applications for patient visits in outpatient clinics. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, relevant studies published between 2021 and 2026 were identified from the Scopus database and systematically analyzed. The review examined four major thematic areas, including factors influencing patient attendance and healthcare utilization, predictive models and analytical techniques, appointment adherence and no-show prediction, and operational applications of predictive analytics. The findings indicate that patient attendance behavior is influenced by a combination of behavioral, organizational, technological, social, and clinical determinants, with historical attendance patterns consistently emerging as the strongest predictor of future healthcare utilization. The review also reveals a methodological shift from traditional statistical approaches toward machine learning and advanced predictive analytics techniques capable of processing complex healthcare datasets. Furthermore, predictive analytics has expanded beyond forecasting functions and increasingly supports resource allocation, scheduling optimization, continuity-of-care management, telemedicine planning, and healthcare service improvement. Despite these advances, challenges related to data quality, model generalizability, external validation, digital inequities, and model interpretability remain significant. Future research should prioritize explainable artificial intelligence, hybrid predictive frameworks, real-time analytics systems, and multicenter validation studies. Overall, predictive analytics represents a powerful decision-support capability that can enhance healthcare efficiency, patient engagement, and service quality within outpatient healthcare environments. ]]>
Copyrights © 2026
